Intelligent Interaction of Fluorine containing materials Based on NL2SQL

Haoyu Liu; Yadong Wu; Weihan Zhang

doi:10.62051/ijcsit.v1n1.25

Authors

Haoyu Liu
Yadong Wu
Weihan Zhang

DOI:

https://doi.org/10.62051/ijcsit.v1n1.25

Keywords:

NL2SQL, Fluorine containing materials, Semantic parsing, Supervised learning

Abstract

The data on the production and development of fluorine containing materials are characterized by a large amount of data and a high degree of dimensionality of physical and chemical property characterization indicators. The manual way of analyzing the data item by item not only has high interaction cost, but also is difficult to analyze and explore the data intuitively. In order to efficiently utilize the data, this paper firstly constructs a dataset of fluorine-containing materials and proposes the Mengzi-ITPT model based on it, which takes Mengzi as the encoder and uses the attention mechanism to enhance the representation of the listed information. Meanwhile, for the data characteristics of fluorine containing materials, the training strategy of ITPT is adopted to improve the accuracy of the model. The experimental results show that the accuracy of the Mengzi-ITPT model query reaches 86.9% when the model is trained under the fluorine-containing material dataset.

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References

Wang, Y. Opportunities and Prospects of Fluorocarbon Application in New Energy Field[J]. new materials industry,2019,(10):3034.DOI:10.19599/j.issn.1008-892x.2019.10.008.

Xiaoyu Z ,Fengjing Y ,Guojie M , et al. M-SQL: Multi-Task Representation Learning for Single-Table Text2sql Generation[J]. IEEE Access,2020,8.

Zhang, Z., Wang, B., Zhao, J., et al. Intelligent Interaction of Power Data Based on NL2SQL[J].Grid technology,2022,46(07):2564-2571.DOI:10.13335/j.1000-3673.pst.2021.1311.

Liu T, Wang K, Sha L, et al. Table-to-text generation by structure-aware seq2seq learning[C]//Proceedings of the AAAI conference on artificial intelligence. 2018, 32(1).

Dong L, Lapata M. Coarse-to-fine decoding for neural semantic parsing[J]. arxiv preprint arxiv:1805.04793, 2018.

Chua L O. CNN: A paradigm for complexity[M]. World Scientific, 1998.

Memory L S T. Long short-term memory[J]. Neural computation, 2010, 9(8): 1735-1780.

Zhang Z, Zhang H, Chen K, et al. Mengzi: Towards lightweight yet ingenious pre-trained models for chinese[J]. arxiv preprint arxiv:2110.06696, 2021.

Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.

Sun C, Qiu X, Xu Y, et al. How to fine-tune bert for text classification?[C]//Chinese Computational Linguistics: 18th China National Conference, CCL 2019, Kunming, China, October 18–20, 2019, Proceedings 18. Springer International Publishing, 2019: 194-206.

Zhong V, Xiong C, Socher R. Seq2sql: Generating structured queries from natural language using reinforcement learning[J]. arxiv preprint arxiv:1709.00103, 2017.

Su J. Simbert: Integrating retrieval and generation into bert[J]. Tech. Rep, 2020.

Yu T, Li Z, Zhang Z, et al. Typesql: Knowledge-based type-aware neural text-to-sql generation[J]. arxiv preprint arxiv:1804.09769, 2018.

Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arxiv preprint arxiv:1810.04805, 2018.

Chang S, Liu P, Tang Y, et al. Zero-shot text-to-SQL learning with auxiliary task[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(05): 7488-7495.

Xu X, Liu C, Song D. Sqlnet: Generating structured queries from natural language without reinforcement learning[J]. arxiv preprint arxiv:1711.04436, 2017.

Hwang W, Yim J, Park S, et al. A comprehensive exploration on wikisql with table-aware word contextualization[J]. arxiv preprint arxiv:1902.01069, 2019.

He P, Mao Y, Chakrabarti K, et al. X-SQL: reinforce schema representation with context[J]. arxiv preprint arxiv:1908.08113, 2019.